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Clinical Phenomapping and Outcomes after Heart Transplantation

  • Author(s): Bakir, Maral
  • Jackson, Nicholas J.
  • Han, Simon
  • Bui, Alex
  • Chang, Eleanor
  • Liem, David A
  • Ardehali, Abbas
  • Baas, Arnold S.
  • Press, Marcella Calfon
  • Cruz, Daniel
  • Deng, Mario C
  • DePasquale, Eugene C
  • Fonarow, Gregg C.
  • Khuu, Tam
  • Kwon, Murray H
  • Kubak, Bernard M
  • Nsair, Ali
  • Phung, Jennifer L.
  • Reed, Elaine F
  • Schaenman, Joanna M
  • Shemin, Richard J
  • Zhang, Qiuheng J.
  • Ardehali, Reza
  • Tseng, Chi-Hong
  • Cadeiras, Martin
  • et al.

Published Web Location

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6064662/
No data is associated with this publication.
Abstract

Background

Survival after heart transplantation (HTx) is limited by complications related to alloreactivity, immune suppression, and side effects of pharmacological therapies. We hypothesize that time-dependent phenomapping of clinical and molecular datasets is a valuable approach to clinical assessments and guiding medical management to improve outcomes.

Methods

We analyzed clinical, therapeutic, biomarker, and outcome data from 94 adult HTx patients and 1557 clinical encounters performed between January 2010 and April 2013. Multivariate analyses were employed to evaluate the association between immunosuppression therapy, biomarkers, and the combined clinical endpoint of death, allograft loss, retransplantation, and rejection. Data were analyzed by K-means clustering (k=2) to identify patterns of similar combined immunosuppression management, and percentile slopes were computed to examine the changes in dosages over time. Findings were correlated with clinical parameters, HLA antibody titers, peripheral blood mononuclear cell gene expression of the AlloMap test genes, and an intragraft, heart tissue gene co-expression network analysis was performed.

Results

Unsupervised cluster analysis of immunosuppressive therapies identified two groups, one characterized by a steeper immunosuppression minimization, associated with a higher likelihood for the combined endpoint, and the other by a less pronounced change. A time-dependent phenomap suggested that patients in the higher event rate group had increased HLA class I and II antibody titers, higher expression of the FLT3 AlloMap gene, and lower expression of the March8 and WDNR40A AlloMap genes. Intramyocardial biomarker-related co-expression network analysis of the FLT3 showed an immune system-related network underlying this biomarker.

Conclusion

Time-dependent precision phenotyping is a mechanistically insightful, data-driven approach to characterize patterns of clinical care and identify ways to improve clinical management and outcomes.

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